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Found 7 papers, 2 papers with code
Representer Point Selection for Explaining Regularized High-dimensional Models
We introduce a novel class of sample-based explanations we term high-dimensional representers, that can be used to explain the predictions of a regularized high-dimensional model in terms of importance weights for each of the training samples.
Faith-Shap: The Faithful Shapley Interaction Index
We show that by additionally requiring the faithful interaction indices to satisfy interaction-extensions of the standard individual Shapley axioms (dummy, symmetry, linearity, and efficiency), we obtain a unique Faithful Shapley Interaction index, which we denote Faith-Shap, as a natural generalization of the Shapley value to interactions.
Heavy-tailed Streaming Statistical Estimation
We consider the task of heavy-tailed statistical estimation given streaming $p$-dimensional samples.
Order-free Learning Alleviating Exposure Bias in Multi-label Classification
In this paper, we propose a new framework for MLC which does not rely on a predefined label order and thus alleviates exposure bias.
Completely Unsupervised Speech Recognition By A Generative Adversarial Network Harmonized With Iteratively Refined Hidden Markov Models
Producing a large annotated speech corpus for training ASR systems remains difficult for more than 95% of languages all over the world which are low-resourced, but collecting a relatively big unlabeled data set for such languages is more achievable.
Adversarial Learning of Label Dependency: A Novel Framework for Multi-class Classification
The discriminator learns to model label dependency by discriminating real and generated label sets.
Transcribing Lyrics From Commercial Song Audio: The First Step Towards Singing Content Processing
Spoken content processing (such as retrieval and browsing) is maturing, but the singing content is still almost completely left out.
